A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics)...

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Bibliographic Details
Published in:Frontiers in neurorobotics Vol. 15; p. 704226
Main Authors: Wei, Baichun, Ding, Zhen, Yi, Chunzhi, Guo, Hao, Wang, Zhipeng, Zhu, Jianfei, Jiang, Feng
Format: Journal Article
Language:English
Published: Lausanne Frontiers Research Foundation 10.08.2021
Frontiers Media S.A
Subjects:
Algorithms
Deep learning
Discriminant analysis
Electromyography
Exoskeleton
Gait
gait recognition
Human mechanics
Kinematics
kinematics prediction
long short-term memory
Machine learning
Methods
motion decoding algorithm
Neuroscience
Pattern recognition
Periodicity
Principal components analysis
Prostheses
User training
ISSN:1662-5218, 1662-5218
Online Access:Get full text
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Summary:The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.
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Edited by: Yuan Zong, Southeast University, China
Reviewed by: Deepak Joshi, Indian Institutes of Technology (IIT), India; Ye Ma, Ningbo University, China
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2021.704226